An acute exacerbation is a frequent occurrence in individuals with chronic obstructive pulmonary disease (COPD), a gradually advancing respiratory condition marked by the decline of lung function and overall quality of life. These acute exacerbations of COPD (AECOPD) are prevalent and often result in hospitalizations and escalated healthcare expenses (1, 2). It is estimated that AECOPD contributes to 6% of global mortality rates and incurs annual costs exceeding $32 billion in the United States (3).

In recent years, there has been an increasing body of evidence suggesting that diet and nutrition may play a significant role in the development of COPD (4). Among patients with COPD, abnormal nutritional status and changes in body composition are prevalent comorbidities that have a detrimental effect on prognosis, including an increased risk of COPD exacerbation, depression, and mortality (5–7). Previous research has indicated that a substantial proportion of COPD patients, ranging from 30 to 60%, suffer from malnutrition (8, 9). Furthermore, elderly individuals are particularly susceptible to these complications as they age (10). Therefore, it is crucial to assess and address the nutritional status of COPD patients.

Despite the fact that malnutrition poses a major global health concern, the clinical diagnostic criteria have not been universally agreed upon (11). The Global Leadership Initiative on Malnutrition (GLIM) has developed a consensus-based report to establish universally applicable criteria for diagnosing malnutrition (12). Furthermore, various quantitative nutritional assessment tools have been employed to screen and evaluate malnutrition. The Geriatric Nutritional Risk Index (GNRI) is a validated nutrition-related index that predicts clinical outcomes in elderly patients (13). Similarly, the Prognostic Nutrition Index (PNI) is utilized to evaluate the nutritional status of surgical patients, anticipate surgical risks, and make prognostic assessments (11). However, the validation of quantitative nutritional tools in relation to the standard malnutrition diagnosis criteria as a reference for patients with AECOPD and its impact on in hospital outcomes remains unexplored.

In this study, the GNRI and PNI were examined for their effectiveness in detecting GLIM-defined malnutrition and predicting prognosis in elderly AECOPD patients.

This research constitutes a single-center observational cohort study, focusing on elderly patients admitted to the First Affiliated Hospital of Zhengzhou University due to acute exacerbation of chronic obstructive pulmonary disease between January 2022 and December 2022. The study received ethical approval from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University.

A total of 839 patients were included in our study. The characteristics of these patients are presented in Table 1. Among the patients diagnosed with AECOPD, the average age was determined to be 72.06 ± 7.54 years, with males comprising 80.5% of the entire participant pool. Additionally, the LOS was found to be 8.96 ± 6.42 days and the hosptialization costs per day was ¥2347.22 ± 5535.20. Furthermore, it was observed that 286 patients (34.1%) met the criteria for malnutrition as defined by the GLIM. The prevalence rates of malnutrition, as determined by the GNRI and the PNI, were found to be 47.4 and 24.7%, respectively.

Table 2 presents the observed variations in characteristics and classical nutritional markers between two distinct groups, which were divided based on three nutritional screening tools. In comparison to the normal group, the malnutritional group identified by GLIM, PNI, and GNRI exhibited significantly higher age and lower body weight, body mass index, albumin, hemoglobin, lymphocyte-to-monocyte ratio and length of stay (p < 0.05). Furthermore, malnourishment was found to be more prevalent among male patients when identified by PNI or GNRI, but not when identified by GLIM. Additionally, malnourishment identified by GNRI, but not PNI or GLIM, was associated with higher hospitalization costs per day.

Table 3 presents the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and area under the curve (AUC) of PNI and GNRI in the identification of malnutrition as defined by GLIM. Both PNI and GNRI demonstrated acceptable diagnostic performance for malnutrition, as evidenced by their respective sensitivity values of 0.741 and 0.895, specificity values of 0.664 and 0.772, and AUC values of 0.702 and 0.834.

The multivariate analysis revealed that malnutrition, as indicated by GNRI (OR = 1.405, 95% CI 1.070–1.846, p = 0.015), PNI (OR = 1.875, 95% CI 1.425–2.468, p < 0.001), and GLIM (OR = 1.376, 95% CI 1.033–1.833, p = 0.029), was significantly associated with an increased risk of exceeding 7-day LOS (Figure 1). Age, sex, and BMI were adjusted as covariates in the multivariable analysis. Collinearity regression results indicated that there was no collinearity between age, sex, and BMI, allowing them to be used as covariates in the analysis. Furthermore, malnutrition identified by GNRI (OR = 1.510, 95% CI 1.097–2.079, p = 0.011) and GLIM (OR = 1.498, 95% CI 1.080–2.080, p = 0.016) was found to be associated with a higher risk of hospitalization costs. However, the malnutrition identified by these three assessment tools did not show any significant association with the CCI.

Patients were assessed using three different tools (GLIM, PNI, and GNRI) to diagnose malnutrition in our study. The results indicated that the rates of nutritional risk in elderly hospitalized populations with AECOPD, as determined by GLIM, PNI, and GNRI, were 28.5, 47.4, and 45.5%, respectively. Previous studies have reported malnutrition rates of 30–60% in COPD patients (8, 9, 17). AECOPD, defined as a sudden exacerbation of respiratory symptoms requiring additional treatment (18, 19), may contribute to a higher nutritional risk compared to patients with typical COPD. In conclusion, our findings are generally consistent with these previous studies.

Malnutrition is characterized as a state of inadequate nutrition that detrimentally impacts the structural or functional capabilities of the body, encompassing a decrease in caloric and protein intake (20). In the context of patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD), the heightened energy expenditure resulting from respiratory effort can contribute to malnutrition. Moreover, humoral factors, including inflammatory cytokines, adipokines, and hormones, may also serve as potential etiological factors for malnutrition in AECOPD patients (21, 22). Additionally, malnutrition may be linked to reduced overall physical activity or diminished appetite stemming from depressive tendencies in individuals with AECOPD (23). The presence of malnutrition has the potential to compromise the patient’s immune defenses and expedite the progression of diseases. Additionally, it can result in a decline in skeletal muscle mass and functionality, a decrease in diaphragm mass and thickness, ultimately culminating in respiratory failure (20). Consequently, it is imperative to promptly address nutritional status and prevent unfavorable prognoses associated with malnutrition.

Despite the widespread acceptance of the GLIM diagnostic criteria for malnutrition as published in 2018, the clinical implementation is deemed inadequate due to the complexity of the steps involved and the lack of clarity in defining factors such as inflammation or disease burden (24).

Furthermore, the prevalence of malnutrition in AECOPD is notably high, yet there has been limited research conducted on nutritional screening tools specifically for this population. This study aimed to examine the efficacy of two quantitative nutritional tools, namely the GNRI and the PNI, in the detection of malnutrition as defined by the GLIM in elderly patients with AECOPD. The GNRI exhibited higher sensitivity (89.5%), satisfactory specificity (77.2%), and a larger AUC value of 0.834. Similarly, the PNI demonstrated good sensitivity (74.1%), specificity (66.4%), and an AUC of 0.702. These findings suggest that either the PNI or GNRI may serve as a viable alternative tool for assessing malnutrition in this patient population. The agreement (k-value = 0.617) between the GNRI and GLIM can be attributed to the inclusion of serum albumin, current weight, and ideal weight in the GNRI. Serum albumin levels have long been considered indicative of nutritional status and systemic inflammation in patients with AECOPD. Consequently, serum albumin serves as a supportive proxy measure of inflammation and is one of the etiologic criteria of GLIM (25, 26). Additionally, the body weight/ideal body weight ratio provides a macroscopic description of skeletal muscle mass (13). In contrast, the PNI solely incorporates two laboratory indicators (serum albumin and lymphocytes), without considering any anthropometric measurements.

In the current investigation, the utilization of GLIM, PNI, and GNRI as diagnostic tools for identifying malnutrition were found to be associated with increased length of stay exceeding 7 days and higher hospitalization costs. However, these diagnostic measures were not found to be predictive of the results of CCI. Several potential explanations may account for these findings. Prior research conducted on hospitalized patients has revealed a significant prevalence of malnutrition upon admission, which has been shown to have a detrimental effect on LOS and contribute to escalated hospitalization costs (27). The analysis of cost estimation data from various countries indicates that the direct cost of treating AECOPD is a substantial portion of the healthcare budget (28–30). Advanced AECOPD commonly presents with multiple comorbidities, leading to a greater symptom burden and poorer outcomes (31). Additionally, malnutrition has been linked to longer hospital stays and higher costs (27), and it also contributes to the progression of AECOPD. However, it is important to note that our findings only suggest a trend rather than statistical significance in the association between malnutrition and an increase in the CCI, potentially due to the limited sample size.

Malnutrition can be detected using GNRI and PNI, which are alternatives to GLIM, in the context of AECOPD. An elevated probability of prolonged hospital stays and escalating hospitalization costs is associated with the malnourished AECOPD. Further research is needed to verify our conclusion.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

The studies involving humans were approved by the Scientific Research and Clinical Trial Ethics Committee of the First Affiliated Hospital of Zhengzhou University. The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from a by- product of routine care or industry. Written informed consent for participation was not required from the participants or the participants' legal guardians/next of kin in accordance with the national legislation and institutional requirements.

XZ: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. YW: Data curation, Methodology, Writing – original draft. MX: Data curation, Resources, Writing – original draft. YZ: Data curation, Resources, Writing – original draft. QL: Supervision, Writing – review & editing.